Light Fields are commonly represented as 4 dimensional functions with 2 spatial and 2 angular dimensions [1]}, [2]}. They can be seen as 2D arrays of images (called sub-aperture images), each having an unlimited depth of field, and differing from their neighbour images only by a slight shift of the view angle. The sampling in the angular dimensions is key in Light Field imaging [3]}. In particular, densely sampled Light Fields make it possible to directly render images with shallow depth of field while controlling the focus depth.
Such rendering, often referred to as Light Field refocusing, does not require knowledge of the scene's geometry. It is usually performed either by shifting and averaging the sub-aperture images [4]} or by selecting a 2D slice in the 4D Fourier domain [5]}.
However, a dense angular sampling comes at the expense of very high requirements in terms of capture, storage, processing power and memory.
A too sparse angular sampling, on the other hand, does not allow for a smooth transition between viewpoints and causes angular aliasing in the refocused images, characterized by sharp structures in the out of focus regions. The importance of a dense angular sampling is clearly shown by the vast literature on viewpoint interpolation. Several approaches exist including depth image based rendering techniques [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, deep learning methods either exploiting a depth map estimation [12]}, [13]} or not [14]}, [15]}, and approaches leveraging sparsity priors of the Light Field data in a transformed domain [16]}, [17]}, [18]}, [19]}.
However, although viewpoint interpolation greatly simplifies the capture of dense Light Fields, it also increases the amount of data to store and process for the final rendering application.
